2. a) Consider the following experimental study on the effectiveness of aspirin in preventing cardiovascular events and answer the questions at the end of the study.

Based on:https://www.ncbi.nlm.nih.gov/pubmed/30158069

Eligible patients were aged 55 years and older who suffered from cardiac disease. Roughly half the patients were males and roughly half the patients were females. Patients were assigned (1:1) with a computer-generated randomisation code to receive enteric-coated aspirin tablets (100 mg) or placebo tablets, once daily.Patients, investigators, and others involved in treatment or data analysis were masked to treatment allocation. The primary efficacy endpoint was a composite outcome of time to first occurrence of cardiovascular death, myocardial infarction, unstable angina, stroke, or transient ischaemic attack.

1. What experimental design did the researchers use, and what are some potential flaws with this experimental design?
2. How could you improve on the experimental design to make the results more robust? (see the document 'experimental design' under Resources)
   

Experimental Design Ideally, your experimental design should: Describe how participants are allocated to experimental groups, A common method is completely randomized design, where participants are assigned to groups at random. A second method is randomized block design, where participants are divided into homogeneous blocks (for example, age groups) before being randomly assigned to groups. Minimize or eliminate confounding variables, which can offer alternative explanations for the experimental results. Allow you to make inferences about the relationship between independent variables and dependent variables. . Reduce variability, to make it easier for you to find differences in treatment outcomes. Design of experiments involves: The systematic collection of data A focus on the design itself, rather than the results Planning changes to independent (input) variables and the effect on dependent variables or response variables Ensuring results are valid, easily interpreted, and definitive. The most important principles: are: Randomization: the assignment of study components by a completely random method, like simple random sampling. Randomization eliminates bias from the results Replication: the experiment must be replicable by other researchers. This is usually achieved with the use of statistics like the standard error of the sample mean or confidence intervals. Blocking: controlling sources of variation in the experimental results. What is a Confounding Variable? A confounding variable is an "extra" variable that you didn't account for. They can ruin an experiment and give you useless results. They can suggest there is correlation when in fact there isn't. They can even introduce bias. That's why it's important to know what one is, and how to avoid getting them into your experiment in the first place. (confounding Variable ) Activity Level Weight Gain (Independent Variable) (Dependent Variable) A confounding variable can have a hidden effect on your experiment's outcome. In an experiment, the independent variable typically has an effect on your dependent variable. For example, if you are researching whether lack of exercise leads to weight gain, lack of exercise is your independent variable and weight gain is your dependent variable. Confounding variables are any other variable that also has an effect on your dependent variable. They are like extra independent variables that are having a hidden effect on your dependent variables. What is a Completely Randomized Design? A completely randomized design (CRD) is an experiment where the treatments are assigned at random. Every experimental unit has the same odds of receiving a particular treatment. This design is usually only used in lab experiments, where environmental factors are relatively easy to control for; it is rarely used out in the field, where environmental factors are usually impossible to control. When a CRD has two treatments, it is equivalent to a t-test A completely randomized design is generally implemented by: 1. Listing the treatment levels or treatment combinations. 2. Assigning each level/combination a random number. 3. Sorting the random numbers in order, to produce a random application order for treatments. However, you could use any method that completely randomizes the treatments and experimental units, as long as you take care to ensure that: The assignment is truly random. Page 1 of 50 You have accounted for W. Completely Randomized Design Example. Let's suppose you were conducting an experiment to see how a type of fertilizer {you have 4 different ones) affects the growth rate of 1 6 tomato plants In a greenhouse. The rst step Is to list the treatment levels. You have four fertilizers, so let's call these ABCD. You have 16 plant locations, labeled 'I-'I. First. write the numbers 1-16 in 16 pieces of equal sized paper and place them into a bowl. Next, write the letters A B C D on 16 separate pieces of paper {Liyou'll have 4 x As, 4 1: Rs, 4 Cs and 4 Ds) and place them in another bowl. Select one piece of paper from the rst bowl and one from the second to get a location and a treatment. Factorial Design. What is a Factorial Design? A factorial experimental design is used to investigate the effect of two or more WM: on one W Elam:- For example, let's say a researcher wanted to investigate components for Increasing W The three components are: 0 SAT intensive class [yes or no]. 5 SAT Prep book [yes or no]. 0 Extra homework [yes or no]. The researcher plans to manipulate each of these independent variables. Each of the independent variables is called afacror, and each factor has two levels (yes or no). As this experiment has 3 factors with 2 levels, this is a 2 x 2 x 2 = 23 factorial design. An experiment with 3 factors and 3 levels would be a 33 factorial design and an experiment with 2 factors and 3 levels would be a Wdesign. mmmaogwm factorial experiments only have two levels. In some experiments where the number of levelffactor combinations are unmanageable, the experiment can be split Into parts (for example, by half), creating a fractional experimental design. Main Effect and Interaction Effect. Two types of effects are considered when analyzing the results from a factorial experiment: unlimited; and W The W is the effect of an independent variable (in this case, SAT prep class or SAT book or extra homework} on the dependent variable (SAT Scores). For a main effect to exist, you'd want to see a consistent trend across the different levels. For example. you might conclude that students who took the SAT prep class scored consistently higher than students who did not. An interaction effect occurs between factors. For example. one group of students who took the SAT class and used the SAT prep book showed an increase in SAT scores while the students who took the class but did not use the book didn't show any Increase. You could infer that there Is an interaction between the SAT class and use of the SAT prep book. mm PaQEZDfS Randomized Block Design htt s: stattrek.com statistics dictiona .as x?denition=randomized%20block%20desi n With a randomized block design. the experimenter divides subjects into subgroups called blocks, such that the variability within blocks is less than the variability between blocks. Then, subjects within each block are randomly assigned to treatment conditions. Compared to a Wm this design reduces variability within treatment conditions and potential confounding, producing a better estimate of treatment effects. The table below shows a randomized block design for a hypothetical medical experiment. 250 250 \" 250 250 Subjects are assigned to blocks, based on gender. Then. within each block. subjects are randomly assigned to treatments (either a placebo or a cold vaccine). For this design, 250 men get the pl acebo. 250 men get the vaccine. 250 women get the placebo, and 250 women get the vaccine. It is known that men and women are physiologically different and react differently to medication. This design ensures that each treatment condition has an equal proportion of men and women. As a result, differences between treatment conditions cannot be attributed to gender. This randomized block design removes gender as a potential source of variability and as a potential confounding variable. Excerpted from:h su'fww.statisticshowto.datasciencecentral.comf erimental-desi What is Matched Pairs Design? Matched pairs design is a special case of randomized block design. In this design, two treatments are assigned to homogeneous groups (blocks) of subjects. The goal is to maximize Wn each pair. In other words, you want the pairs to be as similar as possible, The blocks are composed of matched pairs which are randomly assigned a treatment (commonly the drug or a placebo}. For example, an experiment to test a new drug may have blocks of 200 males and 200 fe males. Each block contains 10:) pairs, who are matched according to some criteria other than sex (like age, other medications, or health conditions}. Each pair is then treated like a block, with each randome assigned to receive the drug or a placebo. The following table shows experiment, where pair 1 could represent two healthy women mas, pair 2 could represent two women age 29 with liver disease, pair 3 could contain two healthy women age 39, pair 4 could contain two women age 39 with liver disease, and so on. AAIAAF .5. : Observational Study What is an Observational Study? An observational study {sometimes called a natural experiment or a quasi-experiment) is where the researcher observes the study participants and measures variables without assigning any treatments. For example, let's say you wanted to find out the effect of cognitive therapy for ADHD. In an experimental study, you would assign some patients cognitive therapy and other patients some other form of treatment (or no treatment at all). In an observational study you would find patients who are already undergoing the 111325 and some who are already pa rticlpatlng in other therapies (or no therapy at all}. Ideally, treatments should be Investigated experimentally with random assignment of treatments to participants. This random assignment means that measured and unmeasured characteristics are evenly divided over the groups. In other words, any differences between the groups would be due to chance. Any statistical tests you run on these types of studies would be reliable. However, it isn't always ethical or feasible to run experimental studies, especially In medical studies involving life- threatening or potentially disabled studies. In these cases, observational studies are used. Examples of Observational Studies Selective Serotonin Reuptoke Inhibitors and Violent Crime: A Cohort Study A study published In Wing studled the uncertain relatlonshlp between SSRIs {like Prozac and Pexll) and Vlclent Crime. The researchers '...extracted infon'nation on SSRIs prescribed in Sweden between 2006 and 2009 from the Swedish Prescribed Drug Register and Information on convictions for violent crimes for the same period from the Swedish national crime register. They then compared the rate of violent crime while Individuals were prescribed SSRIs with the rate of violent crime In the same Individuals while not receiving medication.'I The study findings found an Increased association between SSRI use and violent crimes. (Keener-Air Found to Add 5 Months to Life A Brigham Young University study examined the connected between air quality and life expectancy, The researchers looked at life expectancy data from 51 metropolitan areas and compared the gures to air quality Improvements in each region from the 1980s to 1990s. After taking into account factors like smoking and socioeconomic status, the researchers found that an mad about ve months life expectancy was attributed to clean air. The New York Times printed a summary of the results hate- E'ects of Children of Occupational Exposures to Lead Researchers matched 33 children whose parents were exposed to lead at work with 33 children who were the same age and loved in the same neighborhood. Elevated levels of lead were found in the exposed children. This was attributed to levels of lead that the parents were exposed to at work. and poor hygiene practices of the parent. You can find a summary of this observational study hm- Longitudinal Research Longitudinal research Is an observational study of the same Mover time. Studies can last weeks, months or even decades. The term \"longitudinal" Is very broad, but generally means to collect data over more than one period, from the same War very similar participants). According to sociologist W, the research should also Involve some comparison of data among or between periods. However, the longitudinal research doesn't necessarily have to be collected overtime. Data could be collected at one point in time but Include retrospective data. For example, a participant could be asked about their prior exercise habits up to and including the time of the study. The purpose of Longitudinal Research is to: 1. Record patterns of change. For example. the development of emphysema over time. 2. Establish the direction and magnitude of causal relationships. For example, women who smoke are Wm rlie ofernnhvsema than non-smokers. Cross Sectional Research Cross sectional research involves collecting data at one specific point in time. You can interact with individuals directly, or you could study data in a database or other media. For example, you could study medical databases to see if illegal drug use results in heart disease. If you find a correlation (what is correlation?) between illegal drug use and heart disease, that would support the claim that illegal drug use may increase the risk of heart disease. Cross sectional research is a descriptive study; you only record what you find and you don't manipulate variables like in traditional experiments. It is most often used to look at how often a phenomenon occurs in a population